Particle analysis generally comprises the analysis of cells, nuclei, chromosomes and other particles for the purpose of identifying the particles as members of different populations and/or sorting the particles into different populations. This type of analysis includes automated analysis by means of image and flow cytometry. In either instance, the particle, such as a cell, may be labeled with one or more markers and then examined for the presence or absence of one or more such markers. In the case of a cell, such as a leukocyte, tumor cell or microorganism, the marker can be directed to a molecule on the cell surface or to a molecule in the cytoplasm. Examination of a cell's physical characteristics, as well as the presence or absence of marker(s), provides additional information which can be useful in identifying the population to which a cell belongs.
Cytometry comprises a well known methodology using multi-parameter data for identifying and distinguishing between different cell types in a sample. For example, the sample may be drawn from a variety of biological fluids, such as blood, lymph or urine, or may be derived from suspensions of cells from hard tissues such as colon, lung, breast, kidney or liver. In a flow cytometer, cells are passed, in suspension, substantially one at a time through one or more sensing regions where in each region each cell is illuminated by an energy source. The energy source generally comprises an illumination means that emits light of a single wavelength such as that provided by a laser (e.g., He/Ne or argon) or a mercury arc lamp with appropriate filters. Light at 488 nm is a generally used wavelength of emission in a flow cytometer having a single sensing region.
In series with a sensing region, multiple light collection means, such as photomultiplier tubes (or "PMT"), are used to record light that passes through each cell (generally referred to as forward light scatter), light that is reflected orthogonal to the direction of the flow of the cells through the sensing region (generally referred to as orthogonal or side light scatter) and fluorescent light emitted from the cell, if it is labeled with fluorescent marker(s), as the cell passes through the sensing region and is illuminated by the energy source. Each of forward light scatter (or FSC), orthogonal light scatter (SSC), and fluorescence emissions (FL1, FL2, etc.) comprise a separate parameter for each cell (or each "event"). Thus, for example, two, three or four parameters can be collected (and recorded) from a cell labeled with two different fluorescence markers.
Flow cytometers further comprise data acquisition, analysis and recording means, such as a computer, wherein multiple data channels record data from each PMT for the light scatter and fluorescence emitted by each cell as it passes through the sensing region. The purpose of the analysis system is to classify and count cells wherein each cell presents itself as a set of digitized parameter values. Typically, by current analysis methods, the data collected in real time (or recorded for later analysis) is plotted in 2-D space for ease of visualization. Such plots are referred to as "dot plots" and a typical example of a dot plot drawn from light scatter data recorded for leukocytes is shown in FIG. 1 of U.S. Pat. No. 4,987,086. By plotting orthogonal light scatter versus forward light scatter, one can distinguish between granulocytes, monocytes and lymphocytes in a population of leukocytes isolated from whole blood. By electronically (or manually) "gating" on only lymphocytes using light scatter, for example, and by the use of the appropriate monoclonal antibodies labeled with fluorochromes of different emission wavelength, one can further distinguish between cell types within the lymphocyte population (e.g., between T helper cells and T cytotoxic cells). U.S. Pat. Nos. 4,727,020, 4,704,891, 4,599,307 and 4,987,086 describe the arrangement of the various components that comprise a flow cytometer, the general principles of use and one approach to gating on cells in order to discriminate between populations of cells in a blood sample.
Of particular interest is the analysis of cells from patients infected with HIV, the virus which causes AIDS. It is well known that CD4.sup.+ T lymphocytes play an important role in HIV infection and AIDS. For example, counting the number of CD4.sup.+ T lymphocytes in a sample of blood from an infected individual will provide an indication of the progress of the disease. A cell count under 400 per mm.sup.3 is an indication that the patient has progressed from being seropositive to AIDS. In addition to counting CD4.sup.+ T lymphocytes, CD8.sup.+ T lymphocytes also have been counted and a ratio of CD4:CD8 cells has been used in understanding AIDS.
In both cases, a sample of whole blood is obtained from a patient. Monoclonal antibodies against CD3 (a pan-T lymphocyte marker), CD4 and CD8 are labeled directly or indirectly with a fluorescent dye. These dyes have emission spectra that are distinguishable from each other. (Examples of such dyes are set forth in example 1 of U.S. Pat. No. 4,745,285.) The labeled cells then are run on the flow cytometer and data is recorded. Analysis of the data can proceed in real time or be stored in list mode for later analysis.
While data analyzed in 2-D space can yield discrete populations of cells, most often the dot plots represent projections of multiple clusters. As a result, often it is difficult to distinguish between cells which fall into regions of apparent overlap between clusters. In such cases, cells can be inadvertently classified in a wrong cluster, and thus, contribute inaccuracy to the population counts and percentages being reported. In blood from an HIV infected patient for example, over-inclusion of T cells as being CD4.sup.+ could lead a clinician to believe a patient had not progressed to AIDS, and thus, certain treatment which otherwise might be given could be withheld. In cancers, such as leukemia, certain residual tumor cells might remain in the bone marrow after therapy. These residual cells are present in very low frequencies (i.e., their presence is rare and thus their occurrence in a large sample is a "rare event"), and thus, their detection and classification are both difficult and important.
Current data analysis methods fail to provide sufficient means to discriminate between clusters of cells, and thus, fail to permit more accurate identification and/or sorting of cells into different populations. In addition, such methods fail to predict if the preparative conditions used by the technician were done properly (e.g., improper staining techniques leading to non-specific staining or pipetting improper amounts of reagent(s) and/or sample(s)). Finally, most methods work well for mononuclear preparations from whole blood or on erythrocyte lysed whole blood but perform poorly on unlysed whole blood because of the over abundance of red cells and debris in a sample.